The studies surveyed in this paper examined several types of data categorized into linguistic data, psycholinguistic data, metadata, and interaction data [1]}. Linguistic data was central to a series of NLP applications and includes,for example, authorship attribution and forensic linguistics, gender detection, and
personality type detection [2]}. Many studies show that the linguistic and semantic features of social media users' posts could help indicate and clarify the mental state of the poster [3]}. Mapping words often obtain psycholinguistic features words into pre-defined psychological and affective categories. The Linguistic Inquiry Word Count (LIWC) is one of the most widely used psycholinguistic dictionaries in related NLP tasks[1]}. The LIWC consists of a large number of words along with different categories started by two effective classes (positive, negative emotion) and more than 80 categories (e.g., anxiety, anger, sadness) [2]}, [1]}. The LIWC has been used in different domains such as social relations and mental health [2]}, [8]}.
Metadata features are pieces of information that describe digital data, which can be account metadata or post/message metadata. Account metadata are the data that describe the account, such as the owner's name, profile information, biography, and location. Post or message metadata are the data that describe posts, such as the author, location, likes, number of shares, date/time, links, and hashtags. Interaction data are associated with what users produce in their daily interactions and communication in the digital world [9]}. Several interactive features were examined, which include user temporal posting patterns.
